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MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

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Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#31
post #28

Interesting that they're using TensorFlow. Is TensorFlow still common for new projects or is it because Google is sponsoring the class?

It is not common for new projects. The vast majority of new projects use PyTorch, with some using tensorflow and some using JAX.

Can PyTorch be used to implement LLMs / GPT?

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#32
post #31
post #28

Earlier quoted context omitted.

It is not common for new projects. The vast majority of new projects use PyTorch, with some using tensorflow and some using JAX.

Can PyTorch be used to implement LLMs / GPT?

Yes, see also the transformers library containing many transformer models in pytorch

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#33

Which is the best course or set of videos to learn the basics of neural networks and deep learning? Something that really gets the best explanation of things like backprop?

http://cs231n.stanford.edu/

cs231n is still a really solid course, despite the more recent lecture videos not being publicly available.

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#34

Bit of a side tangent, but why does MIT upload (or allow the upload) of these videos under the staff member's youtube. Alexander Amini in this case. It makes it hard to find and subscribe to. And also a bit weird from an ownership perspective. For better or worse I think it's how Lex Fridman got his initial boost, I believe his personal youtube channel contained some popular MIT lectures of him at the start.

When I worked in higher ed, I learned that professors retain a lot more ownership of their content than I expected. I don’t know if this is how it works everywhere, but I wouldn’t be surprised if that was the case here.

In basically all cases course material is developed by professors and TAs, though the current lecturer might have adapted from several years of previous work. Who owns the IP exactly is tricky (likely the university has at least an equal stake?), but I expect that if lectures are recorded, but not open-access then it's the lecturer's decision. It might be copyright issues, re-use of someone else's slides, not wanting to let students see past years' work, etc.

For example, I work at ETH and we have a large internal video archive of lectures (as do many universities pre- and post-covid), but some lecturers choose to post material on YouTube too. It's not a blanket yes/no policy at the institutional level, as far as I'm aware.

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#35

Which is the best course or set of videos to learn the basics of neural networks and deep learning? Something that really gets the best explanation of things like backprop?

Look for Karpathy’s CS231n lectures on YouTube. They are really good.

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#36
post #21
post #5

[flagged]

Exactly. Unfortunately hackernews these days is huge fans of the military and the richest classes without much question. Psyops don't exist, research journalism is tinfoilhattery and all science is awesome science. Question one state and you're apparently fan of another, it's become the perfect setup for the status quo. In reality the US intelligentsia is tiny and unfathomably powerful, an octopus with its arms deep…

Leave then.

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#37
post #28

Interesting that they're using TensorFlow. Is TensorFlow still common for new projects or is it because Google is sponsoring the class?

It is not common for new projects. The vast majority of new projects use PyTorch, with some using tensorflow and some using JAX.

[deleted]

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#38
post #31
post #28

Earlier quoted context omitted.

It is not common for new projects. The vast majority of new projects use PyTorch, with some using tensorflow and some using JAX.

Can PyTorch be used to implement LLMs / GPT?

PyTorch is the most used neural net framework in research. Almost everything appears in PyTorch first.

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#39

I wonder why that parameter is called "h"? Hmmm ... Why not just say the word "hysteresis" and bring some magnets to class for show-and-tell to help your students develop an intuition for the "h" parameter in RNNs.

Hidden state?

Re: MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention [video]

#40
post #7

I wonder why that parameter is called "h"? Hmmm ... Why not just say the word "hysteresis" and bring some magnets to class for show-and-tell to help your students develop an intuition for the "h" parameter in RNNs.

hysteresis is also important to understand for working with radio networking.

And for non linear forcing of plasmas… but it’s been many years since my phd
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